R: الرسم البياني الأساسي في لغة R

Region    Q1    Q2    Q3    Q4
Beijing  1000  1200  1100  1500
Shanghai  1500  1800  1700  2000
Guangzhou   800   900  1000  1200

# 1. A Line Chart in One Line of Code(4 Quarterly City Trends)
plot(sales, type = "b", col = rainbow(4), main = "Quarterly Sales Trends")

# 2. Creating a bar chart in one line(4 Total City Sales)
barplot(rowSums(sales[, -1]), main = "Total Sales Comparison", col = "skyblue")

# 3. A Line Chart(Percentage)
pie(rowSums(sales[, -1]), labels = rownames(sales))

# Plot a scatter plot of two columns of data
x <- 1:10
y <- x ^ 2
plot(x, y)

plot(x, y,
     type = "p",           # Graph Type
     main = "Title",
     xlab = "X Axis",
     ylab = "Y Axis",
     col = "blue",         # Color
     pch = 19,             # Point-shaped(0-25)
     cex = 1.5,            # Font Size
     xlim = c(0, 12),     # X Shaft Range
     ylim = c(0, 110))    # Y Shaft Range

# For the same set of data, 6 A Painting Technique
x <- 1:5
y <- c(2, 4, 3, 5, 4)

par(mfrow = c(2, 3))  # 2 row 3 Column Layout
for (t in c("p", "l", "b", "o", "h", "s")) {
  plot(x, y, type = t, main = paste("type =", t))
}

# 4 City Scatter Plot
x <- c(1, 2, 3, 4)
y <- c(1000, 1500, 800, 1200)
plot(x, y,
     main = "City Sales Scatter Plot",
     xlab = "City Code",
     ylab = "Sales",
     col = "blue",
     pch = 19)

# Quarterly Trend Chart
quarters <- c("Q1", "Q2", "Q3", "Q4")
sales <- c(1000, 1200, 1100, 1500)

plot(quarters, sales,
     type = "b",     # Point + Line
     main = "Q1-Q4 Sales Trends",
     xlab = "Quarter",
     ylab = "Sales",
     col = "darkblue",
     lwd = 2,        # Line width
     pch = 19)

# 4 City Sales Bar Chart
sales_data <- c(Beijing = 4800, Shanghai = 7000, Guangzhou = 3900, Shenzhen = 4500)

barplot(sales_data,
        main = "Total Sales by City",
        xlab = "City",
        ylab = "Sales",
        col = c("red", "blue", "green", "orange"),
        border = "white",
        horiz = FALSE)  # TRUE Sideways

# Sales Share Pie Chart
sales_data <- c(Beijing = 4800, Shanghai = 7000, Guangzhou = 3900, Shenzhen = 4500)

pie(sales_data,
    main = "Share of Sales",
    col = rainbow(4),
    labels = paste0(names(sales_data), "\n", sales_data))

# 1000 The Distribution of a Random Number
data <- rnorm(1000, mean = 100, sd = 15)

hist(data,
     main = "Normal Distribution",
     xlab = "Value",
     ylab = "Frequency",
     col = "lightblue",
     border = "white",
     breaks = 30)  # min 30 a range

# 4 Class Exam Score Distribution
scores <- list(
  1Cls = c(85, 90, 78, 92, 88, 76, 95, 80),
  2Cls = c(72, 80, 85, 78, 90, 88, 82, 75),
  3Cls = c(95, 88, 92, 90, 85, 78, 80, 88),
  4Cls = c(60, 65, 70, 75, 80, 85, 90, 95)
)

boxplot(scores,
        main = "4 Class Grade Distribution",
        xlab = "Class",
        ylab = "Fractions",
        col = c("red", "blue", "green", "orange"))

# 2x2 Layout
par(mfrow = c(2, 2))

plot(1:10, main = "Plot 1")
plot(1:10, type = "l", main = "Plot 2")
barplot(1:5, main = "Plot 3")
pie(1:4, main = "Plot 4")

# Restore Default Layout
par(mfrow = c(1, 1))

# 1 L + 4 S Layout
layout(matrix(c(1, 1, 2, 3,
                1, 1, 4, 5), nrow = 2, byrow = TRUE))

plot(1:10, main = "Large Image")           # 1
plot(1:5, main = "Small image 1")          # 2
plot(1:5, main = "Small image 2")          # 3
plot(1:5, main = "Small image 3")          # 4
plot(1:5, main = "Small image 4")          # 5

layout(1)  # Restore

# 1. Name
plot(1:5, col = "red")
plot(1:5, col = "blue")
plot(1:5, col = "darkgreen")

# 2. Hexadecimal
plot(1:5, col = "#FF0000")
plot(1:5, col = "#3366CC")

# 3. rainbow() Color Palette
barplot(1:7, col = rainbow(7))
barplot(1:7, col = heat.colors(7))
barplot(1:7, col = topo.colors(7))
barplot(1:7, col = terrain.colors(7))

plot(1:25, pch = 0:25, cex = 2)

# Data
df <- data.frame(x = 1:5, y = c(2, 4, 3, 5, 4))

# Basics R(5 row)
plot(df$x, df$y, type = "b", main = "Trends", xlab = "X", ylab = "Y",
     col = "blue", pch = 19, lwd = 2)

# ggplot2(6 row,**By default, it looks better**)
library(ggplot2)
ggplot(df, aes(x = x, y = y)) +
  geom_line(color = "blue") +
  geom_point(color = "blue", size = 3) +
  labs(title = "Trends", x = "X", y = "Y") +
  theme_minimal()

# ============================================
# 4 Visualization of Quarterly City Sales
# Features:6 Comprehensive Display of Basic Charts
# ============================================

# 1. Prepare data
sales <- matrix(c(
  1000, 1200, 1100, 1500,  # Beijing
  1500, 1800, 1700, 2000,  # Shanghai
   800,  900, 1000, 1200,  # Guangzhou
  1200, 1400, 1300, 1600   # Shenzhen
), nrow = 4, byrow = TRUE)
rownames(sales) <- c("Beijing", "Shanghai", "Guangzhou", "Shenzhen")
colnames(sales) <- c("Q1", "Q2", "Q3", "Q4")
print(sales)

# 2. Settings 2x3 Layout
par(mfrow = c(2, 3), mar = c(4, 4, 3, 1))

# 3. Plot 1:Line Chart(4 City)
plot(1:4, sales[1, ], type = "b", col = "red", ylim = c(0, 2500),
     main = "Quarterly Sales Trends", xlab = "Quarter", ylab = "Sales",
     pch = 19, lwd = 2, xaxt = "n")
axis(1, at = 1:4, labels = colnames(sales))
for (i in 2:4) {
  lines(1:4, sales[i, ], type = "b", col = i, pch = 19, lwd = 2)
}
legend("topleft", legend = rownames(sales),
       col = 1:4, pch = 19, lwd = 2, cex = 0.7)

# 4. Plot 2:Bar Chart(Total Sales)
barplot(rowSums(sales),
        main = "Total Sales by City",
        xlab = "City", ylab = "Total Sales",
        col = c("red", "blue", "green", "orange"),
        border = "white")

# 5. Plot 3:Pie Chart(Percentage)
pie(rowSums(sales),
    main = "Share of Sales",
    col = rainbow(4),
    labels = paste0(rownames(sales), "\n", rowSums(sales)))

# 6. Plot 4:Histogram(Sales Distribution)
hist(sales, main = "Sales Distribution", xlab = "Sales",
     col = "lightblue", border = "white", breaks = 10)

# 7. Plot 5:Box-and-Whisker Plot(By City)
boxplot(sales, main = "Quarterly Sales Breakdown by City",
        xlab = "City", ylab = "Sales",
        col = c("red", "blue", "green", "orange"))

# 8. Plot 6:Stacked Bar Chart
barplot(sales, beside = FALSE,  # beside=FALSE → Stacked
        main = "Quarterly Sales Stacked Chart",
        xlab = "City", ylab = "Sales",
        col = c("red", "blue", "green", "orange"),
        legend.text = colnames(sales))

# 9. Restore Layout
par(mfrow = c(1, 1))

png("myplot.png", width = 800, height = 600)
plot(1:10)
dev.off()